5 Arguments

On entry: the n observations yi, for i=1,2,…,n, assumed to follow a generalized Pareto distribution.

Constraints:

y[i-1]≥0.0;

∑i=1ny[i-1]>0.0.

3:
optopt – Nag_OptimOptInput

On entry: determines the method of estimation, set:

optopt=Nag_PWM

For the method of probability-weighted moments.

optopt=Nag_MOM

For the method of moments.

optopt=Nag_MOMMLE

For maximum likelihood with starting values given by the method of moments estimates.

optopt=Nag_PWMMLE

For maximum likelihood with starting values given by the method of probability-weighted moments.

Constraint:
optopt=Nag_PWM, Nag_MOM, Nag_MOMMLE or Nag_PWMMLE.

4:
xi – double *Output

On exit: the parameter estimate ξ^.

5:
beta – double *Output

On exit: the parameter estimate β^.

6:
asvc[4] – doubleOutput

On exit: the variance-covariance of the asymptotic Normal distribution of ξ^ and β^. asvc[0] contains the variance of ξ^; asvc[3] contains the variance of β^; asvc[1] and asvc[2] contain the covariance of ξ^ and β^.

7:
obsvc[4] – doubleOutput

On exit: if maximum likelihood estimates are requested, the observed variance-covariance of ξ^ and β^. obsvc[0] contains the variance of ξ^; obsvc[3] contains the variance of β^; obsvc[1] and obsvc[2] contain the covariance of ξ^ and β^.

8:
ll – double *Output

On exit: if maximum likelihood estimates are requested, ll contains the log-likelihood value L at the end of the optimization; otherwise ll is set to -1.0.